1. Technical Field
The present disclosure relates to an imaging apparatus and the like used for compressed sensing.
2. Description of the Related Art
In order to capture a color image, information regarding three different wavelength ranges of red (R), green (G), and blue (B), which are three primary colors of light, needs to be obtained. There are color imaging apparatuses that obtain information regarding R, G, and B using three image sensors. In most color imaging apparatuses, however, only one image sensor is included for the purpose of reduction in size and cost. Most color imaging apparatuses, therefore, obtain information regarding R, G, and B with one image sensor.
A method is known in which information regarding one of the three wavelength ranges of R, G, and B is obtained in each pixel and then information regarding the three wavelength ranges of R, G, and B is obtained in each pixel by performing processing called “demosaicing”.
FIG. 19 is a schematic diagram illustrating a Bayer pattern, which is widely used (e.g., U.S. Pat. No. 5,629,734). In the Bayer pattern, half of all pixels are G pixels and R and B pixels each occupy a quarter of all the pixels to mimic visual sensation characteristics of a human eye. Information regarding the three wavelength ranges of R, G, and B is then obtained in all the pixels through demosaicing.
On the other hand, in Japanese Unexamined Patent Application Publication (Translation of PCT Application) No. 2013-511924, a technique is disclosed in which optical filter elements are arranged in a random color pattern, and demosaicing is performed by using a compressed sensing technique for a sample data group.
Techniques relating to imaging and image processing are also disclosed in L. I. Rudin, S. J. Osher, and E. Fatemi, “Nonlinear Total Variation Based Noise Removal Algorithms”, Physica D, vol. 60, pp. 259-268, 1992, S. Ono and I. Yamada, “Decorrelated Vectorial Total Variation”, IEEE Conference on Computer Vision and Pattern Recognition, 2014, J. Ma, “Improved Iterative Curvelet Thresholding for Compressed Sensing and Measurement”, IEEE Transactions on Instrumentation & Measurement, vol. 60, no. 1, pp. 126-136, 2011, M. Aharon, M. Elad, and A. M. Bruckstein, “K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation”, IEEE Transactions on Image Processing, vol. 54, no. 11, pp. 4311-4322, 2006, and D. Kiku, Y. Monno, M. Tanaka, and M. Okutomi, “Minimized-Laplacian Residual Interpolation for Color Image Demosaicking”, IS&T/SPIE Electronic Imaging, 2014. “Nonlinear Total Variation Based Noise Removal Algorithms” relates to an algorithm for removing noise from images. “Decorrelated Vectorial Total Variation” relates to a technique for restoring color images. “Improved Iterative Curvelet Thresholding for Compressed Sensing and Measurement” relates to a restoration method in a compressed sensing technique. “K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation” relates to an algorithm for processing images. “Minim ized-Laplacian Residual Interpolation for Color Image Demosaicking” relates to demosaicing for color images.
In U.S. Pat. No. 5,629,734 and Japanese Unexamined Patent Application Publication (Translation of PCT Application) No. 2013-511924, however, only information regarding one of the three wavelength ranges of R, G, and B is obtained in each pixel of an image sensor. The resolution of a color image obtained as a result of demosaicing, therefore, decreases, and an artifact called “false color” is occurred. In addition, if the amount of information obtained by a plurality of pixels is large, speed at which the information is transmitted decreases, and memory space used to accumulate the information increases.